Learning Robotic Reactive Behaviour from Demonstration via Dynamic Tree
Abstract: Programming a complex robot is difficult, time-consuming and expensive. Learning from Demonstration (LfD) is a methodology where a teacher demonst--rates a task and the robot learns to execute the task. This thesis presents a method which generates reactive robot behaviour learned from demonstration where sequences of action are implicitly coded in a rule-based manner. It also presents a novel approach to find behaviour hierarchy among behaviours of a demonstration.In the thesis, the system learns the activation rule of primitives as well as the association that should be performed between sensor and motor primitives. In order to do so, we use the Playful programming language which is based on the reactive programming paradigm. The underlying rule for the activation of associations is learned using a neural network from demonstrated data. Behaviour hierarchy among different sensor-motor associations is learnt using heuristic logic minimization technique called espresso algorithm. Once relationship among the associations is learnt, all the logical relationships are used to generate a hierarchical tree of behaviours using a novel approach that is proposed in the thesis. This allows us to represent the behaviour in hierarchical way as a set of associations between sensor and motor primitives in a readable script which is deployed on Playful.The method is tested on a simulation by varying the number of targets, showing that the system learns underlying rules for sensor-motor association providing high F1-score for each association. It is also shown by changing the complexity of simulation that the system generalises the solution and the knowledge learnt from a sensor-motor association is transferable with all the instances of that association.
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